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Remote Sens. 2015, 7(3), 2781-2807; doi:10.3390/rs70302781

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
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Academic Editors: Lars T. Waser and Prasad S. Thenkabail
Received: 22 December 2014 / Revised: 11 February 2015 / Accepted: 4 March 2015 / Published: 10 March 2015
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Abstract

Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001–2007 and 2007–2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite’s comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today’s continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. View Full-Text
Keywords: CLASlite; deforestation; Global Forest Change dataset; Gola Rainforest National Park; Landsat; sub-pixel classification; tropical forests CLASlite; deforestation; Global Forest Change dataset; Gola Rainforest National Park; Landsat; sub-pixel classification; tropical forests
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lui, G.V.; Coomes, D.A. A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring. Remote Sens. 2015, 7, 2781-2807.

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